This section contains a summary of the key metrics and assumptions that define the RCP architecture: emissions trajectories and concentrations, energy use, population, air pollutants and land use, and the consequent radiative forcing and temperature anomalies specified by each of the four RCP pathways.

The data employed in the development of the RCPs is drawn from the published literature. Each RCP was developed by an Integrated Assessment Modeling (IAM) group, whose published scenario papers were consistent with the base criteria for a particular RCP. Each team then surveyed and created synthesis data sets from available representative studies, which were reviewed repeatedly by different stakeholders. The final agreed set of RCPs was published:

Table 2: RCP-specific publications and model group responsible.

For comprehensive discussions of development methodologies and complete technical information on any RCP, please see the further reading section at the end of this guide.

An important note about socio-economic data

The underlying assumptions about socio-economic trajectories and priorities are not consistent between the RCPs. This quote from van Vuuren 2011 makes clear this point (emphasis added):

“The RCPs were selected from the existing literature on the basis of their emissions and associated concentration levels. This implies that the socio-economic assumptions of the different modeling teams were based on individual model assumptions made within the context of the original publication, and that there is no consistent design behind the position of the different RCPs relative to each other for these parameters.

Scenario development after the RCP phase will focus on developing a new set of socio-economic scenarios. Therefore, socio-economic parameters have not been included in the RCP information available for download. Still, this information does form part of the underlying individual scenario development, and thus provides useful information on internal logic and the plausibility of each of the individual RCPs”

van Vuuren et. al, 2011

Socio-economic data does not form any part of the RCP database. Please note that in this guide, as in van Vuuren 2011, the primary socio-economic characteristics are discussed here only in the context of the RCP development.

RCP Primary Characteristics

RCP 8.5 was developed using the MESSAGE model and the IIASA Integrated Assessment Framework by the International Institute for Applied Systems Analysis (IIASA), Austria. This RCP is characterized by increasing greenhouse gas emissions over time, representative of scenarios in the literature that lead to high greenhouse gas concentration levels (Riahi et al. 2007).

RCP6 was developed by the AIM modeling team at the National Institute for Environmental Studies (NIES) in Japan. It is a stabilization scenario in which total radiative forcing is stabilized shortly after 2100, without overshoot, by the application of a range of technologies and strategies for reducing greenhouse gas emissions (Fujino et al. 2006; Hijioka et al. 2008).

RCP 4.5 was developed by the GCAM modeling team at the Pacific Northwest National Laboratory’s Joint Global Change Research Institute (JGCRI) in the United States. It is a stabilization scenario in which total radiative forcing is stabilized shortly after 2100, without overshooting the long-run radiative forcing target level (Clarke et al. 2007; Smith and Wigley 2006; Wise et al. 2009).

RCP2.6 was developed by the IMAGE modeling team of the PBL Netherlands Environmental Assessment Agency. The emission pathway is representative of scenarios in the literature that lead to very low greenhouse gas concentration levels. It is a “peak-and-decline” scenario; its radiative forcing level first reaches a value of around 3.1 W/m2 by mid-century, and returns to 2.6 W/m2 by 2100. In order to reach such radiative forcing levels, greenhouse gas emissions (and indirectly emissions of air pollutants) are reduced substantially, over time (Van Vuuren et al. 2007a). (Characteristics quoted from van Vuuren et.al. 2011)

RCP Information, Data Types and Resolutions

The following table shows the data types available for the RCPs, the sectors by which emissions are broken down, and the geographical resolution of the information:

Table 3: from van Vuuren et.al. 2011

Emissions and concentrations, forcings and temperature anomalies

Each Representative Concentration Pathway (RCP) defines a specific emissions trajectory and subsequent radiative forcing (a radiative forcing is a measure of the influence a factor has in altering the balance of incoming and outgoing energy in the Earth-atmosphere system, measured in watts per square metre):

The forcing trajectories are consistent with socio-economic projections unique to each RCP. For example, RCP2.6 (RCP3PD) assumes that through drastic policy intervention, greenhouse gas emissions are reduced almost immediately, leading to a slight reduction on today’s levels by 2100. The worst case scenario - RCP8.5 - assumes more or less unabated emissions.

RCP Emission Trajectories

Figure 8: Emissions of main greenhouse gases across the RCPs. Grey area indicates the 98th and 90th percentiles (light/dark grey) of the literature…The dotted lines indicate four of the SRES marker scenarios. Note that the literature values are not harmonized (from van Vuuren et.al. 2011). Click image for larger version

“The CO2 emissions of the four RCPs correspond well with the literature range, which was part of their selection criterion (Fig. 8). The RCP8.5 is representative of the high range of non-climate policy scenarios. Most non-climate policy scenarios, in fact, predict emissions of the order of 15 to 20 GtC by the end of the century, which is close to the emission level of the RCP6. The forcing pathway of the RCP4.5 scenario is comparable to a number of climate policy scenarios and several low-emissions reference scenarios in the literature, such as the SRES B1 scenario. The RCP2.6 represents the range of lowest scenarios, which requires stringent climate policies to limit emissions.

“The trends in CH4 and N2O emissions are largely due to differences in the assumed climate policy along with differences in model assumptions (Fig. 8). Emissions of both CH4 and N2O show a rapidly increasing trend for the RCP8.5 (no climate policy and high population). For RCP6 and RCP4.5, CH4 emissions are more-or-less stable throughout the century, while for RCP2.6, these emissions are reduced by around 40%. The low emission trajectories for CH4 are a net result of low cost emission options for some sources (e.g. from energy production and transport), and a limited reduction for others (e.g. from livestock)” (van Vuuren et.al. 2011)

“The greenhouse gas concentrations in the RCPs closely correspond to the emissions trends discussed earlier . For CO2, RCP8.5 follows the upper range in the literature (rapidly increasing concentrations). RCP6 and RCP4.5 show a stabilizing CO2 concentration (close to the median range in the literature). Finally, RCP2.6 has a peak in CO2 concentrations around 2050, followed by a modest decline to around 400 ppm CO2, by the end of the century. For CH4 and N2O, the order in which the RCPs can be placed are also a direct result of the assumed level of climate policy. The trends in CH4 concentrations are more pronounced, as a result of the relatively short lifetime of CH4. Emission reductions, as in the RCP2.6 and RCP4.5, therefore, may lead to an emission peak much earlier in the century. For N2O, in contrast, a relatively long lifetime and a modest reduction potential imply an increase in concentrations, in all RCPs. For both CH4 and N2O, the concentration levels correspond well with the range in the literature. Further information on the calculations of concentration can be found in Meinshausen et al. (2011b)” (van Vuuren et.al. 2011).

Atmospheric Air Pollutants

Figure 10: Emissions of SO2 and NOX across the RCPs. Grey area indicates the 90th percentile of the literature (only scenarios included in Van Vuuren et al. 2008b, i.e. 22 scenarios; the scenarios were also harmonized for their starting year—but using a different inventory). Dotted lines indicate SRES scenarios. The different studies use slightly different data for the start year. (van Vuuren et.al. 2011) Click image for larger version

“The RCPs generally exhibit a declining trend of air polluting emissions. The emission trends for air pollutants are determined by three factors: the change in driving forces (fossil- fuel use, fertilizer use), the assumed air pollution control policy, and the assumed climate policy (as the last induces changes in energy consumption leading to changes (generally reductions) in air polluting emissions). We have illustrated the trends in air pollutants by looking at SO2 and NOx (Fig. 10). In general, similar trends can be seen for other air pollutants.

“All RCPs include the assumption that air pollution control becomes more stringent, over time, as a result of rising income levels. Globally, this would cause emissions to decrease, over time—although trends can be different for specific regions or at particular moments in time. A second factor that influences the results across the RCPs is climate policy. In general, the lowest emissions are found for the scenario with the most stringent climate policy (RCP2.6) and the highest for the scenario without climate policy (RCP8.5), although this does not apply to all regions, at all times”. (van Vuuren et.al. 2011).

Radiative Forcing Trends

Figure 11: Trends in radiative forcing (left), cumulative 21st centuryCO2 emissions vs 2100 radiative forcing (middle) and 2100 forcing level per category (right). Grey area indicates the 98th and 90th percentiles (light/dark grey) of the literature. The dots in the middle graph also represent a large number of studies. Forcing is relative to pre-industrial values and does not include land use (albedo), dust, or nitrate aerosol forcing (van Vuuren 2011). Click image for larger version

Population and GDP

Figure 12: Population and GDP projections of the four scenarios underlying the RCPs (van Vuuren et.al. 2011). Grey area for population indicates the range of the UN scenarios (low and high) (UN 2003). Grey area for income indicates the 98th and 90th percentiles (light/dark grey) of the IPCC AR4 database (Hanaoka et al. 2006). The dotted lines indicate four of the SRES marker scenarios. Click image for larger version

“The population and GDP pathways underlying the four RCPs are shown in Fig. 12. The figure also shows, as reference, the UN population projections and the 90th percentile range of GDP scenarios in the literature on greenhouse gas emission scenarios. Figure 12 shows the RCPs to be consistent with these two references. It should be noted that, with one exception (RCP8.5), the modeling teams deliberately made intermediate assumptions about the main driving forces (as illustrated by their position in Fig. 12)…In contrast, the RCP8.5 was based on a revised version of the SRES A2 scenario; here, the storyline emphasizes high population growth and lower incomes in developing countries”. (van Vuuren et.al. 2011).

“For energy use, the scenarios underlying the RCPs are consistent with the literature— with the RCP2.6, RCP4.5 and RCP6 again being representative of intermediate scenarios in the literature (resulting in a primary energy use of 750 to 900 EJ in 2100, or about double the level of today).

“The RCP8.5, in contrast, is a highly energy-intensive scenario as a result of high population growth and a lower rate of technology development”. (van Vuuren et.al. 2011).

Energy sources at years 2000 and 2100

Figure 14: Energy sources by sector (van Vuuren et.al. 2011)

“In terms of the mix of energy carriers, there is a clear distinction across the RCPs given the influence of the climate target. Total fossil- fuel use basically follows the radiative forcing level of the scenarios; however, due to the use of carbon capture and storage (CCS) technologies (in particular in the power sector), all scenarios, by 2100, still use a greater amount of coal and/or natural gas than in the year 2000. The use of oil stays fairly constant in most scenarios, but declines in the RCP2.6 (as a result of depletion and climate policy).

The use of non-fossil fuels increases in all scenarios, especially renewable resources (e.g. wind, solar), bio-energy and nuclear power. The main driving forces are increasing energy demand, rising fossil-fuel prices and climate policy. An important element of the RCP2.6 is the use of bio-energy and CCS, resulting in negative emissions (and allowing some fossil fuel without CCS by the end of the century)”. (van Vuuren et.al. 2011).

Land Use

Figure 15: Land use (crop land and use of grass land) across the RCPs. Grey area indicates the 90th percentile of scenarios reported in the literature (taken from Smith et al. 2010). Vegetation is defined as the part not covered by cropland or anthropogenically used grassland. (van Vuuren et.al. 2011). Click image for larger version

“A crucial element of the new scenarios is land use. Land use influences the climate system in many different ways including direct emissions from land-use change, hydrological impacts, biogeophysical impacts (such as changes in albedo and surface roughness), and the size of the remaining vegetation stock (influencing CO2 removal from the atmosphere). Historically, cropland and anthropogenic use of grassland have both been increasing, driven by rising population and changing dietary patterns. There are far fewer land-use scenarios published in the literature than emission or energy-use scenarios. Moreover, far less experience exists with scenario projections (Rose et al. 2011; Smith et al. 2010). Most projections focus on a shorter time period (up to 2030 or 2050) and show an increasing demand for cropland and pasture.

“The limited experience in global land-use modeling as part of integrated assessment work is also reflected in the RCP development process. Compared to emission modeling, definitions of relevant variables and base year data differ more greatly across the IAMs for the land use components.

“The RCPs cover a very wide-range of land-use scenario projections. This is illustrated by the trends shown in Fig. 15 (i.e. after harmonization). The use of cropland and grasslands increases in RCP8.5, mostly driven by an increasing global population. Cropland also increases in the RCP2.6, but largely as a result of bio-energy production. The use of grassland is more-or-less constant in the RCP2.6, as the increase in production of animal products is met through a shift from extensive to more intensive animal husbandry. The RCP6 shows an increasing use of cropland but a decline in pasture. This decline is caused by a similar trend as noted for RCP2.6, but with a much stronger implementation. Finally, the RCP4.5 shows a clear turning point in global land use based on the assumption that carbon in natural vegetation will be valued as part of global climate policy. As a result of reforestation programs, the use of cropland and grassland decreases, following considerable yield increases and dietary changes”. (van Vuuren et.al. 2011).

Figure 16 shows the CO2 emissions and radiative forcing trajectories for each of the four extensions of the RCPs (ECPs). As explained in the method sections, these have not been based on integrated assessment modeling, but on simple extension rules consistent with the rationale of each of the RCPs to which they connect. This has resulted in a set of extended concentration pathways to be used for climate model runs.

An on-line resource of this guide is now available here: www.skepticalscience.com/rcp.php

The author would like to thank Detlef Van Vuuren and Allison Thomson for their helpful suggestions, and to John Cook for the cover artwork for the PDF version of this guide.

This guide quotes extensively from The representative concentration pathways: an overview, van Vuuren et. al. 2011, a special report first published by Climatic Change, and made available through Springerlink.com as an Open Access document, distributed under the terms of the Creative Commons Attribution Non-commercial License which permits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

All other works excerpted or quoted here are copyright their respective authors/publishers.

Copyright

This document is published under the terms of the Creative Commons Attribution 3.0 Unported License, which permits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Written by Graham Wayne (http://gpwayne.wordpress.com) for Skeptical Science, August 2013. PDF cover artwork by John Cook.